Ilya Sutskever
AI researcher; OpenAI co-founder and CEO of Safe Superintelligence (SSI)
Profile
- Nationality
- Born in Russia (then USSR); grew up in Israel; moved to Canada as a teenager
- Field
- Deep learning, neural network language processing, AI safety
- Career
- Research scientist, Google Brain; OpenAI co-founder, research director and chief scientist (2015–2024)
- Now (Oct 2026)
- Co-founder and CEO, Safe Superintelligence Inc. (SSI)
- Major awards
- Fellow of the Royal Society (2022); honorary doctorate, University of Toronto (2025)
Ilya Sutskever was born in Russia (then part of the Soviet Union), grew up in Israel and moved to Canada as a teenager. He received a bachelor's degree in mathematics from the University of Toronto in 2005, and then a master's and a PhD in computer science from the same university. His PhD supervisor was Geoffrey Hinton. He was part of DNNResearch, a company set up by Hinton's research group, and after Google acquired it he worked as a research scientist at Google Brain. In 2015 he co-founded OpenAI. As of October 2026, he is co-founder and chief executive officer (CEO) of the AI company Safe Superintelligence (SSI).
In 2012 Sutskever built AlexNet, a convolutional neural network, with Hinton and Alex Krizhevsky. AlexNet identified objects in a large image database far more accurately than other methods of the time. In 2014, with his Google colleagues Oriol Vinyals and Quoc V. Le, he published the 'sequence to sequence' learning method. In this method one neural network turns a sentence into a set of numbers and another network produces a new sentence from those numbers; it was tested on translating English into French. At OpenAI he served as research director and later as chief scientist, and took part in developing the GPT models.
Sutskever left OpenAI in May 2024 and in June that year founded SSI with Daniel Gross and Daniel Levy. SSI started with offices in Palo Alto in the United States and Tel Aviv in Israel, and described its aim as 'one goal and one product: a safe superintelligence'. Superintelligence means AI with abilities beyond those of humans. After Gross left the company at the end of June 2025, Sutskever became CEO in July. As of July 2026 SSI had not released any commercial products and was focusing on research, and that month it announced a long-term partnership with NVIDIA. He was elected a Fellow of the Royal Society in the UK in 2022 and received an honorary doctorate from the University of Toronto in 2025.
Sources for this profile (11) · as of Oct 22, 2026
- Ilya Sutskever, a leader in AI and its responsible development, receives U of T honorary degree · University of Toronto
- Introducing OpenAI · OpenAI
- Ilya Sutskever to leave OpenAI, Jakub Pachocki announced as Chief Scientist · OpenAI
- Sutskever Launches New AI Company · Pure AI
- Ilya Sutskever will lead Safe Superintelligence following his CEO's exit · TechCrunch
- Sutskever takes over as CEO of Safe Superintelligence · Calcalist (CTech)
- Ilya Sutskever's Safe Superintelligence Inc. and NVIDIA Announce Long-Term Strategic Partnership · NVIDIA Newsroom
- Ilya Sutskever's Safe Superintelligence partners with Nvidia to scale its AI research · TechCrunch
- Sequence to Sequence Learning with Neural Networks · arXiv (Sutskever, Vinyals, Le – Google)
- Ilya Sutskever (profile) · Computer History Museum
- Dr Ilya Sutskever FRS (Fellows Directory) · The Royal Society
AIMade by AIThis profile was written by AI (Claude) from the 11 sources above (official pages, filings, news reports).How it's made
Records 7
Newest first. Open one to see it with its source.
The shift from the age of scaling to the age of research became the dominant frame of 2026 discourse
Scaling skepticism came not from a critic but from one of the founders of the scaling paradigm. Peak data became an industry-standard term
The first public statement of skepticism about pretraining scaling
The organizational form itself — heading straight for superintelligence without shipping products — is an argument. Sparked a debate over whether safety can be separated from commercial pressure
Formalized the encoder-decoder that maps a sequence to a sequence. The ancestor of today's generative model interface
Established in a paper the combination of GPUs, large-scale data and deep CNNs. The conventional starting point of the current deep learning era
The turning point at which, with a wide margin over second place, deep CNNs became the mainstream methodology of computer vision